Optimizing Peptides in TensorFlow 2

A guest post by of MITFigure 1. Conceptually, you can think of and , a machine learning framework based on TensorFlow. Conceptually, you can think of Peptimizer as generating a sequence of amino acids, then predicting a property of the peptide, then optimizing the sequence.

Peptimizer can be used for the optimization of functionality (other than cell-penetrating activity as well) and synthetic accessibility of polymers. We use topological representations of monomers (amino acids) and matrix representations of polymer chains (peptide sequences) to develop interpretable (attribute the gain in property to a specific monomer and/or chemical substructure) machine learning models. The choice of representation and model architecture enables inference of biochemical design principles, such as monomer composition, sequence length or net charge of polymer, by using gradient-based attribution methods.

Key challenges for applying machine learning to advance functional peptide design include limited dataset size (usually less than 100 data points), choosing effective representations, and the ability to explain and interpret models.

Here, we use a dataset of peptides received from our experimental collaborators to demonstrate the utility of the codebase.

Optimization of functionality

Based on our to train on a custom dataset. The scripts for the individual components have been designed in a modular fashion and can be modified with relative ease.

Optimization of synthetic accessibility

Apart from functionality optimization, of a wild-type sequence (Figure 2). The framework consists of a multi-modal convolutional neural network predictor and a brute force optimizer. The predictor is trained over experimental synthesis parameters such as pre-synthesized chain, incoming monomer, temperature, flow rate, and catalysts. The optimizer evaluates single-point mutants of the wild-type sequence for higher theoretical yield.

The choice of a brute force optimizer for optimization of synthetic accessibility is based on the linearly growing sequence space (m x n) for the variations of the wild-type sequence. This sequence space is relatively small in comparison to the exponentially growing sequence space (mⁿ) encountered in optimization of functionality.

This framework may be adapted for other stepwise chemical reaction platforms with in-line monitoring by specifying the different input and output variables and respective data types. It can be accessed using aFigure 2. Outline of synthetic accessibility optimization.

Interpretability of models

A key feature of Figure 3. (left) Positive gradient activation heatmap, and (right) activated chemical substructure, for functional peptide sequence.

Outlook

Optimization of functional polymers using. In addition, the attribution methods will provide insights into the high-dimensional sequence-activity relationships and elucidation of design principles.

Experimental collaboration

This work was done in collaboration with the lab of Bradley Pentelute (Department of Chemistry, MIT). The collaborators for the optimization of functionality and synthetic accessibility were Carly Schissel and Dr. Nina Hartrampf, respectively. We thank them for providing the dataset, experimental validation, and the discussion during the development of the models.

Acknowledgment

We would like to acknowledge the support of Thiru Palanisamy and Josh Gordon at Google for their help with the blog post collaboration and with providing active feedback.